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Inspector gaze-guided multitask learning for explainable structural damage assessment
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AI
DOI：10.1111/mice.70131.png)
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Abstract
En 中文
Accurately classifying damage levels from structural inspection images is critical for automated infrastructure assessment. Although deep neural networks achieve impressive performance, their black-box nature limits explainability, and prior studies using Grad-CAM often yield coarse or inaccurate saliency maps. To overcome these limitations, this paper introduces XIDLE-Net, a multitask model that simultaneously performs damage classification and saliency map prediction to enhance explainability in structural damage assessment. Combining a Swin Transformer encoder with a convolutional neural network decoder, XIDLE-Net is trained with dual supervision using damage labels and inspector gaze-derived attention maps, enhancing both classification accuracy and model explainability. Experimental results show that XIDLE-Net outperforms state-of-the-art methods in both classification and saliency explainability, achieving 78.1% accuracy, 94.3% area under the curve (AUC), and a 39.7% improvement in saliency prediction over ResNet-50 with Grad-CAM. To our knowledge, this is one of the first investigations to employ large-scale inspector gaze data for supervision and to quantitatively evaluate Grad-CAM in structural image classification. The results highlight the promise of human gaze data for advancing explainable vision-based structural health monitoring.
Keywords:
CONVOLUTIONAL NEURAL-NETWORKS
DEEP
CLASSIFICATION
Journal
C
IF:
9.1
Papers:
2.0K
Citations:
10.0K